code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Train a model for image classification."
      )
      parser.add_argument(
          "--data-dir",
          type=str,
          default="~/.mxnet/datasets/imagenet",
          help="training and validation pictures to use.",
      )
      parser.add_argument(
          "--rec-train",
          type=str,
          default="~/.mxnet/datasets/imagenet/rec/train.rec",
          help="the training data",
      )
      parser.add_argument(
          "--rec-train-idx",
          type=str,
          default="~/.mxnet/datasets/imagenet/rec/train.idx",
          help="the index of training data",
      )
      parser.add_argument(
          "--rec-val",
          type=str,
          default="~/.mxnet/datasets/imagenet/rec/val.rec",
          help="the validation data",
      )
      parser.add_argument(
          "--rec-val-idx",
          type=str,
          default="~/.mxnet/datasets/imagenet/rec/val.idx",
          help="the index of validation data",
      )
      parser.add_argument(
          "--use-rec",
          action="store_true",
          help="use image record iter for data input. default is false.",
      )
      parser.add_argument(
          "--batch-size",
          type=int,
          default=32,
          help="training batch size per device (CPU/GPU).",
      )
      parser.add_argument(
          "--dtype",
          type=str,
          default="float32",
          help="data type for training. default is float32",
      )
      parser.add_argument(
          "--num-gpus",
          type=int,
          default=0,
          help="number of gpus to use.",
      )
      parser.add_argument(
          "-j",
          "--num-data-workers",
          dest="num_workers",
          default=4,
          type=int,
          help="number of preprocessing workers",
      )
      parser.add_argument(
          "--num-epochs",
          type=int,
          default=3,
          help="number of training epochs.",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.1,
          help="learning rate. default is 0.1.",
      )
      parser.add_argument(
          "--momentum",
          type=float,
          default=0.9,
          help="momentum value for optimizer, default is 0.9.",
      )
      parser.add_argument(
          "--wd",
          type=float,
          default=0.0001,
          help="weight decay rate. default is 0.0001.",
      )
      parser.add_argument(
          "--lr-mode",
          type=str,
          default="step",
          help="learning rate scheduler mode. options are step, poly and cosine.",
      )
      parser.add_argument(
          "--lr-decay",
          type=float,
          default=0.1,
          help="decay rate of learning rate. default is 0.1.",
      )
      parser.add_argument(
          "--lr-decay-period",
          type=int,
          default=0,
          help="interval for periodic learning rate decays. default is 0 to disable.",
      )
      parser.add_argument(
          "--lr-decay-epoch",
          type=str,
          default="40,60",
          help="epochs at which learning rate decays. default is 40,60.",
      )
      parser.add_argument(
          "--warmup-lr",
          type=float,
          default=0.0,
          help="starting warmup learning rate. default is 0.0.",
      )
      parser.add_argument(
          "--warmup-epochs",
          type=int,
          default=0,
          help="number of warmup epochs.",
      )
      parser.add_argument(
          "--last-gamma",
          action="store_true",
          help="whether to init gamma of the last BN layer in each bottleneck to 0.",
      )
      parser.add_argument(
          "--mode",
          type=str,
          help="mode in which to train the model. options are symbolic, imperative, hybrid",
      )
      parser.add_argument(
          "--model",
          type=str,
          required=True,
          help="type of model to use. see vision_model for options.",
      )
      parser.add_argument(
          "--input-size",
          type=int,
          default=224,
          help="size of the input image size. default is 224",
      )
      parser.add_argument(
          "--crop-ratio",
          type=float,
          default=0.875,
          help="Crop ratio during validation. default is 0.875",
      )
      parser.add_argument(
          "--use-pretrained",
          action="store_true",
          help="enable using pretrained model from gluon.",
      )
      parser.add_argument(
          "--use_se",
          action="store_true",
          help="use SE layers or not in resnext. default is false.",
      )
      parser.add_argument(
          "--mixup",
          action="store_true",
          help="whether train the model with mix-up. default is false.",
      )
      parser.add_argument(
          "--mixup-alpha",
          type=float,
          default=0.2,
          help="beta distribution parameter for mixup sampling, default is 0.2.",
      )
      parser.add_argument(
          "--mixup-off-epoch",
          type=int,
          default=0,
          help="how many last epochs to train without mixup, default is 0.",
      )
      parser.add_argument(
          "--label-smoothing",
          action="store_true",
          help="use label smoothing or not in training. default is false.",
      )
      parser.add_argument(
          "--no-wd",
          action="store_true",
          help="whether to remove weight decay on bias, and beta/gamma for batchnorm layers.",
      )
      parser.add_argument(
          "--teacher",
          type=str,
          default=None,
          help="teacher model for distillation training",
      )
      parser.add_argument(
          "--temperature",
          type=float,
          default=20,
          help="temperature parameter for distillation teacher model",
      )
      parser.add_argument(
          "--hard-weight",
          type=float,
          default=0.5,
          help="weight for the loss of one-hot label for distillation training",
      )
      parser.add_argument(
          "--batch-norm",
          action="store_true",
          help="enable batch normalization or not in vgg. default is false.",
      )
      parser.add_argument(
          "--save-frequency",
          type=int,
          default=10,
          help="frequency of model saving.",
      )
      parser.add_argument(
          "--save-dir",
          type=str,
          default="params",
          help="directory of saved models",
      )
      parser.add_argument(
          "--resume-epoch",
          type=int,
          default=0,
          help="epoch to resume training from.",
      )
      parser.add_argument(
          "--resume-params",
          type=str,
          default="",
          help="path of parameters to load from.",
      )
      parser.add_argument(
          "--resume-states",
          type=str,
          default="",
          help="path of trainer state to load from.",
      )
      parser.add_argument(
          "--log-interval",
          type=int,
          default=50,
          help="Number of batches to wait before logging.",
      )
      parser.add_argument(
          "--logging-file",
          type=str,
          default="train_imagenet.log",
          help="name of training log file",
      )
      parser.add_argument(
          "--use-gn",
          action="store_true",
          help="whether to use group norm.",
      )
      # additional arguments for gradient compression
      parser.add_argument(
          "--compressor", type=str, default="", help="which compressor"
      )
      parser.add_argument(
          "--ef", type=str, default="", help="which error-feedback"
      )
      parser.add_argument(
          "--compress-momentum",
          type=str,
          default="",
          help="which compress momentum",
      )
      parser.add_argument(
          "--onebit-scaling",
          action="store_true",
          default=False,
          help="enable scaling for onebit compressor",
      )
      parser.add_argument(
          "--k", default=1, type=int, help="topk or randomk"
      )
      parser.add_argument(
          "--fp16-pushpull",
          action="store_true",
          default=False,
          help="use fp16 compression during pushpull",
      )
  
      opt = parser.parse_args()
      return opt
  
file_path: byteps/example/mxnet/train_gluon_imagenet_byteps_gc.py
imports: import argparse
  import logging
  import math
  import os
  import subprocess
  import time
  import gluoncv
  import mxnet
  import numpy
  from gluoncv.data import imagenet
  from gluoncv.model_zoo import get_model
  from gluoncv.utils import LRScheduler, LRSequential, makedirs
  from mxnet import autograd
  from mxnet import gluon, nd
  from mxnet.gluon.data.vision import transforms
  import byteps.mxnet
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: opt